Monte Carlo guided Diffusion for Bayesian linear inverse problems

1 code implementation • 15 Aug 2023 • Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines

Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging.

Bayesian Inference

NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform

1 code implementation • NeurIPS 2021 • Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian Robert

Sampling from a complex distribution $\pi$ and approximating its intractable normalizing constant $\mathrm{Z}$ are challenging problems.

State and parameter learning with PaRIS particle Gibbs

no code implementations • 2 Jan 2023 • Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson

The particle-based, rapid incremental smoother PaRIS is a sequential Monte Carlo (SMC) technique allowing for efficient online approximation of expectations of additive functionals under the smoothing distribution in these models.